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Neural Networks Approaches for Combinatorial Optimization Problems

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Handbook of Combinatorial Optimization

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Abstract

Most of the engineering design problems and applications can be formulated as a nonlinear programming problem in which the objective function is nonlinear and has many local optima in its feasible region. It is desirable to find a local optimum that corresponds to the global optimum. The problem of finding the global optimum is known as the global optimization problem. Most such global optimization problems are difficult to solve. The main difficulties in finding the global optimum are that there are no operationally useful optimality conditions for identifying whether a point is indeed a global optimum, except in cases of special structured problems [33] and so it is computationally intensive to obtain the global optimum. Therefore, it is desirable and sometimes necessary to find a near global optimum in a reasonable time rather than obtaining the global optimum.

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  1. School of Industrial Engineering, University of Oklahoma, 202 West Boyd, Room 124, Norman, OK, 73019, USA

    Theodore B. Trafalis & Suat Kasap

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  1. Theodore B. Trafalis
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  2. Suat Kasap
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  1. Department of Computer Science, University of Minnesota, USA

    Ding-Zhu Du

  2. Institute of Applied Mathematics, Academia Sinica, P. R. China

    Ding-Zhu Du

  3. Department of Industrial and Systems Engineering, University of Florida, USA

    Panos M. Pardalos

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Trafalis, T.B., Kasap, S. (1999). Neural Networks Approaches for Combinatorial Optimization Problems. In: Du, DZ., Pardalos, P.M. (eds) Handbook of Combinatorial Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3023-4_5

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  • DOI: https://doi.org/10.1007/978-1-4757-3023-4_5

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